Issue 
Sci. Tech. Energ. Transition
Volume 79, 2024
Decarbonizing Energy Systems: Smart Grid and Renewable Technologies



Article Number  68  
Number of page(s)  9  
DOI  https://doi.org/10.2516/stet/2024066  
Published online  27 September 2024 
Regular Article
Dayahead optimal dispatching for airportintegrated energy system based on loadstorage coordination
College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
^{*} Corresponding author: fangrm@hqu.edu.cn
Received:
7
July
2024
Accepted:
11
August
2024
Airports are characterized by high energy consumption, with the cooling supply load being the primary contributor. Optimal dispatching by utilizing the elastic characteristics of cooling supply load is the key to energy saving and decarbonization of airports. Firstly, this paper proposes to build a loadstorage integrated energy system architecture by incorporating the ice storage system; secondly, a dayahead optimal dispatching model to minimize operating costs and carbon emissions is established; finally, taking an airport as the example, the NSGAII algorithm is applied to solving the model, and the optimal solution for each energy equipment output plan is obtained. The proposed model is compared and analysed under the base scenario, photovoltaic output fluctuation and outdoor temperature variation scenarios, and the daily photovoltaic consumption rate and indoor temperature range are used to evaluate the dispatching results. The optimal dispatching model improves its performance greatly compared to the design condition, with a reduction of 33.3% for the operating cost and 31.9% for the carbon emissions, and the results demonstrate that the proposed model can almost achieve full photovoltaic consumption while ensuring that the indoor temperature is maintained in the passengers’ comfort zone in the face of different levels of photovoltaic output fluctuations and outdoor temperatures. Research outcomes in this paper fill the gap in the optimised potential exploitation of cooling supply loads, respond to the dispatching pressure caused by the energy consumption characteristics of airports, and provide a reference for renewable energy access and energy flexibility management in airports.
Key words: Integrated energy system for airports / Cooling supply load elasticity / Ice storage system / Loadstorage coordination / Photovoltaic consumption / Comfort degree
© The Author(s), published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1 Introduction
As energyintensive infrastructure, airports require a significant amount of energy consumption for air conditioning, lighting, gates, and other equipment [1, 2]. Airports are also notable sources of carbon dioxide emissions [3], accounting for 0.05% of global energyrelated carbon dioxide emissions [4] according to the International Energy Agency. Therefore, optimal dispatching of the Airport Integrated Energy System (AIES) is crucial for achieving dualcarbon goals and is a current research hotspot.
Integrating renewable energy has become the key to energy conservation and carbon emission reduction at airports. The research included in [5] proposed a new microgrid processing scheme for airports by combining solar, hydrogen and energy storage, significantly reducing the annual cost and carbon emissions. The new energy generation proportion in Beijing Daxing International Airport reached 10% [5, 6] established a spatialtemporal passenger distribution model, and the calculated of the HVAC system’s energy consumption than the existing model reduced by 11.3%. For the Copenhagen Airport, a method is proposed in [7] to ensure that the photovoltaic panel’s reflections do not jeopardize flight safety so that the airport can rely on photovoltaic power generation to meet part of its energy needs. However, as a renewable energy source, the volatility and intermittency of photovoltaic power generation will bring challenges to the safe and stable operation of AIES, and the consumption of renewable energy output is not included in the above studies.
Realizing load flexibility management is significant for improving the airport energy systems efficiency [8]. In AIES, the cooling supply load is a kind of elastic load with a large inertia constant coefficient, which is manifested as the response of the cooling supply system is delayed from the load change, thus buffering the direct impact of the external temperature change on the indoor environment, and reshaping the load curve through the reasonable adjustments can reduce the energy consumption of airports [9]. The cooling supply load occupies a large proportion of the energy system of airports, especially in the southern regions, the demand for cooling supply load in summer accounts for 8085% of the total cooling energy load [10], presenting significant potential for efficiency optimization. The studies in [11] proposed a nearzeroenergy airport concept for the airport’s central airconditioning system, which maximizes the energy utilization of the airport by adjusting the fluid temperature in the cooling grid. Prasetyo et al. [12] and Arifin et al. [13] conducted research through computational fluid dynamics modelling and solar radiation intensity change simulation respectively, and the results proved that the optimisation of the fluid parameters in the cooling grid can improve the electric conversion efficiency of photovoltaic panels. The above studies further demonstrate the feasibility of optimizing the cooling grid to enhance energy efficiency in AIES with high photovoltaic integration.
Maintaining indoor temperature to ensure passenger comfort is crucial in airport energy application and management [7]. Integrating Ice Storage System (ISS) to improve the flexibility of the cooling system has become an important way to achieve loadstorage coordination in AIES. Grid stability is achieved by controlling the ice storage/melting decisions of the Ice Storage Device (ISD) in the ISS [14]. Shi et al. [15] analyzed the performance of the ISS in an international airport and found that integrating ISS reduces annual operating costs by 51.3%. By improving the building information modelling platform, Kurniawan et al. [16] can obtain accurate optimisation objective values of the studied buildings, and the research provides an effective means for aggregating optimization techniques in large building models. Facing AIES with extremely high energy consumption, how to satisfy the system energy supply requirements through dispatching while guaranteeing safe and stable operation is the main challenge in dayahead dispatching [8]. Studies on optimal loadstorage coordination in AIES for hot and humid climates are limited, especially regarding ISS characteristics, photovoltaic consumption, and passenger comfort, with insufficient focus on cooling supply loads.
This paper firstly constructs the AIES architecture including ISS; then, combining the “high generation and low storage” characteristics of the ISS and cooling supply load elasticity, a dayahead optimal dispatching model of AIES based on loadstorage coordination was proposed to cope with the high energy consumption and strict indoor environmental control requirements in the hot and humid southern region; finally, the NSGAII algorithm is used to optimizes the balance between economic and environmental benefits. A large international airport in southern China is taken as an example to verify the proposed model’s effectiveness.
The subsequent sections of the manuscript are organized as follows: Section 2 models the constructed AIES and its internal units. Section 3 models the dayahead optimal dispatching based on cooling supply load elasticity. Section 4 analyzes the obtained results by setting up different scenarios. Section 5 concludes the paper.
2 Integrated energy system architecture for the airport incorporating ice storage system
The AIES with ISS as a loadstorage coordination module is shown in Figure 1. The source side includes the power grid, gas grid and Photovoltaic (PV) [17]. Multiple energy sources are coupled via the Combined Cooling Heating and Power (CCHP) unit, which includes an Absorption Chiller (AC), gas turbine, gas boiler and waste heat boiler. The loadside comprises electric, heat, and cooling loads, with cooling loads divided into rigid (e.g., navigation systems) and elastic (e.g., cooling supply loads with high inertia) [18]. Demand response is considered on the electric load side. The storage side mainly includes ISS.
Fig. 1 Integrated energy system architecture for airport. 
2.1 Ice storage system modelling
In the hot southern climate, especially during summer, airports have a high cooling supply demand. Thus, ISS is integrated into the AIES to coordinate load and storage for efficient operation. ISS consists of Electric Chiller (EC), AC, ISD and auxiliary equipment [19]. It stores ice during low electricity price periods and melts it during peak demand or high price periods. ISD operates in two modes: ice storage charging and ice melting discharging, as defined by equations (1) and (2) [20]:(1) (2)where: δ_{ice} is the ISD loss limit; u_{ice} is a 01 variable; P_{ice,c}(t) and P_{ice,d}(t) are the ISD charge/discharge power at hour t; and are ISD maximum charge/discharge power; and are the stored energy after/before the mode conversion; and are the lower/upper limit of the stored energy of the ISD; Q_{ice,c}(t) and Q_{ice,d}(t) are the charge/discharge capacity of the ISD at hour t; a_{c}, b_{c}, c_{c}, d_{c}, a_{d}, b_{d}, c_{d}, d_{d} are regression coefficients.
2.2 Renewable energy modelling
The Beta distribution is used to simulate the solar irradiation intensity of the day to build the PV output model [21], as shown in equations (3) and (4):(3) (4)where: P_{PV}(t) is the output power of the PV panels at hour t; A is the PV array area; η is the photoelectric conversion efficiency. f(S) is the probability density function of the Beta distribution; Γ(·) is the gamma function; S and S_{max} are the actual and the maximum irradiation intensity; α and β are the shape parameters.
2.3 CCHP unit modelling
The model of CCHP is shown in equation (5).(5)where: P_{c}(t), P_{e}(t) and P_{h}(t) are the cooling, electric and thermal output power of the CCHP at hour t; P_{ng}(t) is the natural gas input power at hour t; η_{c}, η_{e} and η_{h} are the cooling, electric and thermal efficiency [22] of the CCHP.
2.4 Cooling supply load elasticity analysis
During the cooling process, the temperature change of the chilled medium usually lags behind that of the cooling medium, maintaining room comfort for a period after cooling stops [23]. In addition, passengers’ temperature perception is elastic [24], allowing minor changes in cooling temperature without affecting comfort. Based on the above characteristics, utilizing Cooling Supply Load Elasticity (CSLE) characteristics can improve AIES operating economy. Accordingly, the Predicted Mean Vote (PMV) index was introduced to evaluate the indoor temperature comfort level to quantify the comfort level of passengers in the system. The PMV is based on the ASHRAE Thermal Awareness Scale for predicting the average response of a population [25], and is calculated as equation (6) after a simplified engineering treatment:(6)where: MR is the human metabolic rate; I_{cl} is the thermal resistance of clothing; t_{s} is the average human skin; and t_{a} is the air temperature around the body.
The PMV index corresponds to the seven thermal sensations of the human body on a 7level scale, and the PMV value of 0 corresponds to the optimal thermal comfort state of the human body [26]. Daytime demands higher comfort due to frequent passenger activity, while nighttime allows for relaxed comfort standards. Accordingly, the timesharing limit of PMV is set, as shown in Figure 2.
Fig. 2 Regulatory range of PMV values. 
3 Dayahead optimal dispatching model for integrated energy system at airport considering CSLE
3.1 Objective function
Dayahead optimal dispatching model takes the lowest operating cost and the smallest environmental cost as the optimal dispatching objectives.
3.1.1 Operating cost
The minimum operating cost is shown in equations (7) and (8), including the purchase and sale cost C_{grid}(t), the penalty cost for solar curtailment C_{loss}(t), the maintenance cost C_{m}(t), and the revenue from electricity sales C_{sell}(t):(7) (8)where: T is the total dispatching periods; C_{e} and C_{g} are the electricity and natural gas prices; P_{in}(t) is the power input at hour t; k_{i} and P_{i}(t) are the unit maintenance cost and output power of each device at hour t; and P_{out}(t) is the active power sold to the grid at hour t.
The C_{loss}(t) is caused by the phenomenon of failing to fully utilize PV, for which the segmentation penalty coefficient g_{p}(t) is introduced [27] in equation (9):(9)where: A_{PV}(t) is the discarded power at hour t; is the discard rate at hour t.
3.1.2 Environmental cost
Environmental costs consist primarily of emissions from grid interactions [28], as shown in equation (10):(10)where: A_{PV}(t) is the discarded power at hour t; is the discard rate at hour t.
3.2 Optimizing variable
The output plan of each energy equipment is selected as the optimal dispatching variable. To highlight the influence of CSLE and due to its high proportion in the overall cooling load, the cooling supply load is mainly considered in the analysis. In the conventional cooling grid, the power balance of the cooling system is equationally constrained, as shown in equation (11).(11)where: C_{EC}(t) is the output power of the EC at hour t; L_{C}(t) is the cooling supply load of the system at hour t.
After considering CSLE, the cooling capacity is transformed from a fixed value to an interval value, as shown in equation (12).(12)where: L_{C}(t)_{min} and L_{C}(t)_{max} are the lower/upper limit of the cooling supply load value under the premise of meeting the comfort level of passengers; k_{down} and k_{up} are the elasticity coefficients of the cooling supply loads.
3.3 Constraint condition
3.3.1 Solar curtailment power constraints
(13)where: is the minimum acceptable PV output power at hour t; is the maximum allowable solar curtailment ratio.
3.3.2 ISS operational constraints
(14)where: P_{EC}(t) is the input power of EC at hour t; P_{WHB}(t) is the output power of the waste heat boiler in CCHP at hour t; COP_{EC} and COP_{AC} are the performance coefficients of EC and AC; C_{AC}(t) is the output power of AC at hour t.
The ISD is an important device for the ISS to realize the loadstorage coordination and needs to satisfy the constraints shown in equation (15):(15)where: S_{ice}(t) is the ISD cool energy stored in the ISD at hour t; γ_{Q} is the selfloss coefficient; η_{ice,c} and η_{ice,d} are the efficiency coefficients of ice storage and melting.
3.3.3 Power balance of the electrical system
(16)where: DR(t) is the change in electrical load after demand response; P_{Loss}(t) is the electrical loss at hour t; η_{t} is the transformer efficiency; P_{grid} is the electricity interacting with the grid.
3.3.4 Power balance of the natural gas system in CCHP
(17)where: G_{GT}(t) and G_{GB}(t) are the gas consumption power of the gas turbine and gas boiler in the CCHP unit at hour t respectively.
3.3.5 Power balance of the heating system
(18)where: L_{H}(t) is the heat load of the system at hour t.
3.3.6 Power balance of the cooling system
As described by equation (12) .
3.4 Model solution
The NSGAII algorithm is used to solve the AIES dayahead optimal dispatching model proposed in this paper. In the process of genetic evolution, the NSGAⅡ algorithm takes the initial feasible solution of population size as the starting point [29] and selects the compromise optimal solution by plotting the Pareto front [30].
4 Case analysis
The dispatching period is 24 h with a 1hour step size. The system cost parameters and ISS parameters are listed in Tables 1 and 2. The system load prediction and outdoor temperature for a typical day in summer are shown in Figure 3, indicating the cooling supply load at 45.88% of the total energy demand. The realtime electricity price [31] trend and daily PV forecast curve are shown in Figure 4. Electricity is sold at a constant price of 0.39 CNY/kWh.
Fig. 3 AIES load forecast curves and outdoor temperatures. 
Fig. 4 Realtime price trend and PV output forecast curves. 
System cost parameters.
Ice storage system parameters.
4.1 Optimization results
The set of Pareto solutions and optimal compromise solutions are shown in Figure 5. When carbon emissions fall below 15,700 kg, operating costs increase significantly for the same carbon reduction. Therefore, based on the Pareto front congestion degree [32], the optimal compromise solution is selected with equal weights for operating and environmental costs.
Fig. 5 Distribution of pareto solution set and optimal solution. 
The total system cost comparison is shown in Table 3, and the cooling grid dispatching balance results are shown in Figure 6. ISS participates in charges ice with maximum power at the storageside during low cooling load and low electricity price periods; ISS melts ice and discharges energy with the maximum power in order to satisfy the demand of loadside cool energy during the period of 11:00–13:00 when both the cooling supply load and electricity price are in the peak periods.
Fig. 6 Power balance of the cooling system. 
Optimal dispatching results considering CSLE.
4.2 Analysis of PV consumption in different scenarios
The model proposed in equation (4) is used to obtain PV output predictions of different fluctuation degrees based on the predicted PV output of typical summer day by changing the standard deviation of the Beta distribution.

Scenario 1: Base scenario.
Typical summer outdoor temperature data and PV output forecast data were used.

Scenario 2: Large fluctuation scenario.
Rapidly changing weather conditions are simulated by amplifying the standard deviation to 2σ.

Scenario 3: Small fluctuation scenario.
Stable weather conditions are simulated by narrowing the standard deviation to 0.5σ.
The total PV output, the PV power for each scenario, and the operating mode and output of the ISS are shown in Figures 7 and 8. During peak cooling load from 10:00 to 13:00 (see Fig. 3), ISS operates in the icemelting and discharging mode to respond to the loadside cooling supply load. During low cooling load periods from 3:00–8:00 and 17:00–18:00, ISS operates in icestorage and charging mode to reduce system costs and to realize the adjustment function of the integrated chargestorage module.
Fig. 7 Total PV power and power of PV output under Scenarios 1, 2 and 3. 
Fig. 8 ISS output power under Scenarios 1, 2 and 3. 
After integrating the ISS with dispatching, the daily PV consumption ratios of Scenario 1, Scenario 2, and Scenario 3 are 99.87%, 99.59%, and 99.73% respectively, which basically realize the full consumption of PV output.
4.3 Analysis of passenger comfort in different scenarios
In summer, the indoor temperature comfort range of the airport is 23.9–27.3 °C [33]. Based on scenario 1, the mean daily temperature (average of the highest/lowest temperatures of the day) is selected to characterize the environmental temperature.

Scenario 4: High temperature scenario.
Select the mean daily temperature of 34.45 °C, simulating hot weather conditions.

Scenario 5: Lowtemperature scenario.
Select the mean daily temperature of 24.72 °C, simulating relatively cool weather conditions.
Figure 9 shows indoor cooling temperatures for each scenario, with the comfort zone marked by orange dotted lines. All three scenarios maintain temperatures within this zone. Scenario 1 is used for specific analysis.
Fig. 9 Indoor cooling temperature under Scenarios 1, 4, and 5. 
The cooling supply load changes before and after considering CSLE are shown in equation (12), with corresponding indoor temperature and PMV value changes in Figure 10. The daytime indoor temperature of the airport ranges from 24.1 °C to 27.2 °C, with PMV values from −0.11 to 0.92; after the 21st hour, the PMV value shows a fluctuating tendency of decreasing, then increasing, then decreasing, with the variation range of −0.11 to −0.84, corresponding to the indoor temperature variation range of 24.1–25.8 °C. These results indicate that after considering CSLE, a larger PMV value is selected to ensure higher comfort during the daytime when passenger activities are frequent; at night, the passengers are not sensitive to the perception of temperature at this time, so the PMV value is mostly negative. The airport’s cooling supply load curve is optimized by moderately adjusting the PMV value while ensuring indoor cooling comfort.
Fig. 10 Scenario 1: a) PMV and indoor temperature curves, b) cooling supply load curves. 
5 Conclusion
This paper integrates ISS into the AIES as a chargestorage module, combining with CSLE for dayahead optimal dispatching based on loadstorage coordination.

Integrating ISS into the system as a loadstorage coordination module, and constructing an integrated energy system architecture suitable for largescale international airports in the southern region by combining the rapid switching process of ISD’s working mode with the elastic characteristics of the cooling supply load.

The high proportion of cooling supply loads and their elasticity are considered, introducing the PMV index to quantify passenger comfort, and establishing a loadstorage coordination dayahead optimal dispatching model. Compared with routine dispatching, the proposed model reduces the operating cost and carbon emission of the system by 16.8% and 19.0%.

By setting up different scenarios for comparison and validation, the proposed model can achieve the full consumption of photovoltaic while ensuring that the indoor cooling temperature is within the passenger’s comfort temperature range, provided that the system supply requirements are met. The results demonstrate the feasibility and effectiveness of the proposed model.
The objective functions and optimization approaches used for the model proposed in this paper will be refined in the future to enhance the optimization effect.
Acknowledgments
This work was supported by the Natural Science Foundation of Xiamen (3502Z202373052).
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All Tables
All Figures
Fig. 1 Integrated energy system architecture for airport. 

In the text 
Fig. 2 Regulatory range of PMV values. 

In the text 
Fig. 3 AIES load forecast curves and outdoor temperatures. 

In the text 
Fig. 4 Realtime price trend and PV output forecast curves. 

In the text 
Fig. 5 Distribution of pareto solution set and optimal solution. 

In the text 
Fig. 6 Power balance of the cooling system. 

In the text 
Fig. 7 Total PV power and power of PV output under Scenarios 1, 2 and 3. 

In the text 
Fig. 8 ISS output power under Scenarios 1, 2 and 3. 

In the text 
Fig. 9 Indoor cooling temperature under Scenarios 1, 4, and 5. 

In the text 
Fig. 10 Scenario 1: a) PMV and indoor temperature curves, b) cooling supply load curves. 

In the text 
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